swinv2-base-patch4-window12-192-22k-ConcreteClassifier-PVT

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window12-192-22k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.9423
• Accuracy: {'accuracy': 0.6160830090791181}
• F1: {'f1': 0.6252886431691335}
• Precision: {'precision': 0.6429213069076691}
• Recall: {'recall': 0.6390705914040895}

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.001
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.2473	1.0	1927	0.9809	{'accuracy': 0.5909208819714656}	{'f1': 0.5910435702573883}	{'precision': 0.6661654194259725}	{'recall': 0.6016064778916844}
1.7959	2.0	3854	1.8580	{'accuracy': 0.26848249027237353}	{'f1': 0.17635435227079216}	{'precision': 0.15600300783137744}	{'recall': 0.2748312652287413}
1.8437	3.0	5781	1.9243	{'accuracy': 0.1743190661478599}	{'f1': 0.1111482743173735}	{'precision': 0.1516816437642854}	{'recall': 0.1887752678966561}
1.803	4.0	7708	1.9003	{'accuracy': 0.1846952010376135}	{'f1': 0.1316379146314846}	{'precision': 0.1460659091286986}	{'recall': 0.1928584239028949}
1.8121	5.0	9635	1.7386	{'accuracy': 0.33229571984435796}	{'f1': 0.24134878979912178}	{'precision': 0.2476900084001273}	{'recall': 0.3378007007551934}
1.7984	6.0	11562	1.7322	{'accuracy': 0.3014267185473411}	{'f1': 0.2074421859419775}	{'precision': 0.17325128260235972}	{'recall': 0.31234162186318526}
1.7385	7.0	13489	1.6964	{'accuracy': 0.3120622568093385}	{'f1': 0.2220020856519651}	{'precision': 0.24821744277779434}	{'recall': 0.3239827067963708}
1.578	8.0	15416	1.6265	{'accuracy': 0.2827496757457847}	{'f1': 0.20468330816120264}	{'precision': 0.23605832742279897}	{'recall': 0.2983260879145022}
1.7397	9.0	17343	1.4545	{'accuracy': 0.41686121919584956}	{'f1': 0.38005456072209515}	{'precision': 0.38606801407175106}	{'recall': 0.43686617410103795}
1.9688	10.0	19270	1.5583	{'accuracy': 0.3761348897535668}	{'f1': 0.3427169207054117}	{'precision': 0.3765571735514816}	{'recall': 0.3992290557525892}
1.5298	11.0	21197	1.3465	{'accuracy': 0.46718547341115435}	{'f1': 0.4188714217488438}	{'precision': 0.41426640127137376}	{'recall': 0.4948604123766437}
1.7964	12.0	23124	2.1154	{'accuracy': 0.2059662775616083}	{'f1': 0.15046942523030551}	{'precision': 0.24607297755729998}	{'recall': 0.1965173812427065}
1.6734	13.0	25051	1.8294	{'accuracy': 0.26718547341115434}	{'f1': 0.23757487469054722}	{'precision': 0.2619241634609933}	{'recall': 0.28087243077801927}
1.4376	14.0	26978	1.3830	{'accuracy': 0.4254215304798962}	{'f1': 0.41718777684401953}	{'precision': 0.4554477836203232}	{'recall': 0.450319904911865}
1.3403	15.0	28905	1.2193	{'accuracy': 0.4926070038910506}	{'f1': 0.44282153705015465}	{'precision': 0.5137836037175402}	{'recall': 0.5162930270137864}
1.4806	16.0	30832	1.2119	{'accuracy': 0.5060959792477302}	{'f1': 0.49960050025350783}	{'precision': 0.4999152354253829}	{'recall': 0.5224114833278971}
1.2526	17.0	32759	1.1265	{'accuracy': 0.532295719844358}	{'f1': 0.5154889606383344}	{'precision': 0.5564411969038197}	{'recall': 0.5477056677098556}
1.3673	18.0	34686	1.0845	{'accuracy': 0.5553826199740597}	{'f1': 0.5594361896393714}	{'precision': 0.5621230845137732}	{'recall': 0.5721227129539878}
1.241	19.0	36613	1.1499	{'accuracy': 0.5317769130998703}	{'f1': 0.5281353810835646}	{'precision': 0.5775200417239811}	{'recall': 0.5464467844676986}
1.2881	20.0	38540	1.0368	{'accuracy': 0.5732814526588845}	{'f1': 0.5822889712524011}	{'precision': 0.5902075134144437}	{'recall': 0.5939011675173421}
1.1817	21.0	40467	1.0927	{'accuracy': 0.562905317769131}	{'f1': 0.5595449409010723}	{'precision': 0.6241213136239965}	{'recall': 0.5901142810563812}
1.0632	22.0	42394	0.9835	{'accuracy': 0.5916990920881972}	{'f1': 0.5977863969792085}	{'precision': 0.6133139141030883}	{'recall': 0.6180557435733788}
1.3852	23.0	44321	0.9644	{'accuracy': 0.6108949416342413}	{'f1': 0.6180791606479925}	{'precision': 0.6280310097271309}	{'recall': 0.6298244322284886}
0.9998	24.0	46248	1.0270	{'accuracy': 0.5730220492866407}	{'f1': 0.5857906297181984}	{'precision': 0.6037780108566702}	{'recall': 0.5926557023955539}
1.2504	25.0	48175	0.9579	{'accuracy': 0.6041504539559014}	{'f1': 0.6089085687671967}	{'precision': 0.6356079019561951}	{'recall': 0.6279729594034652}
0.9085	26.0	50102	0.9158	{'accuracy': 0.6256809338521401}	{'f1': 0.634525318707402}	{'precision': 0.638920861622337}	{'recall': 0.6478580836265876}
1.0098	27.0	52029	0.9567	{'accuracy': 0.598443579766537}	{'f1': 0.6064064760408924}	{'precision': 0.6229095253118345}	{'recall': 0.6214139916724217}
1.0409	28.0	53956	0.9464	{'accuracy': 0.611413748378729}	{'f1': 0.6211699172289087}	{'precision': 0.6387402688839067}	{'recall': 0.634161757892515}
1.3289	29.0	55883	0.9405	{'accuracy': 0.6176394293125811}	{'f1': 0.6271514140166757}	{'precision': 0.6447687702321006}	{'recall': 0.6400894012742261}
0.8946	30.0	57810	0.9423	{'accuracy': 0.6160830090791181}	{'f1': 0.6252886431691335}	{'precision': 0.6429213069076691}	{'recall': 0.6390705914040895}

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0
• Datasets 2.17.1
• Tokenizers 0.15.2